Computational Intelligence in Power Engineering / Edition 1by Ajith Abraham
Pub. Date: 09/21/2010
Publisher: Springer Berlin Heidelberg
Computational Intelligence (CI) is one of the most important powerful tools for research in the diverse fields of engineering sciences ranging from traditional fields of civil, mechanical engineering to vast sections of electrical, electronics and computer engineering and above all the biological and pharmaceutical sciences. The existing field has its origin in the… See more details below
Computational Intelligence (CI) is one of the most important powerful tools for research in the diverse fields of engineering sciences ranging from traditional fields of civil, mechanical engineering to vast sections of electrical, electronics and computer engineering and above all the biological and pharmaceutical sciences. The existing field has its origin in the functioning of the human brain in processing information, recognizing pattern, learning from observations and experiments, storing and retrieving information from memory, etc. In particular, the power industry being on the verge of epoch changing due to deregulation, the power engineers require Computational intelligence tools for proper planning, operation and control of the power system. Most of the CI tools are suitably formulated as some sort of optimization or decision making problems. These CI techniques provide the power utilities with innovative solutions for efficient analysis, optimal operation and control and intelligent decision making.
This edited volume deals with different CI techniques for solving real world Power Industry problems. The technical contents will be extremely helpful for the researchers as well as the practicing engineers in the power industry.
Table of Contents
Robust Design of Power System Stabilizers for Multimachine Power Systems Using Differential Evolution.- An AIS-ACO Hybrid Approach for Multi-Objective Distribution System Reconfiguration.- Intelligent Techniques for Transmission Line Fault Classification.- Fuzzy Reliability Evaluations in Electric Power Systems.- Load Forecasting and Neural Networks: A Prediction Interval-Based Perspective.- Neural Network Ensemble for 24-Hour Load Pattern Prediction in Power System.- Power System Protection Using Machine Learning Technique.- Power Quality.- Particle Swarm Optimization PSO: A New Search Tool in Power System and Electro Technology.- Particle Swarm Optimization and Its Applications in Power Systems.- Application of Evolutionary Optimization Techniques for PSS Tuning.- A Metaheuristic Approach for Transmission System Expansion Planning.
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